Online vs offline evaluation distinguishes production scoring of live AI interactions from pre-release testing on a fixed dataset. Offline evaluation compares versions against expected behavior before deployment, while online evaluation monitors real traffic for quality, safety, and failure patterns that curated test cases did not anticipate.
LangSmith’s evaluation concepts describe offline datasets and online production traces as complementary parts of one improvement loop.
When should you use offline evaluation?
Use an AI evaluation harness before release to compare prompts, models, retrieval, or workflow logic against stable examples. Offline tests provide repeatability and reference outputs, which makes them suitable for regression gates and calibrated LLM judges.
What does online evaluation add?
Production data exposes new inputs, changing behavior, and failures the test set missed. LLM observability supplies the sampled runs, while AI agent evals can inspect the full agent trajectory. The useful loop sends online failures back into the offline dataset, verifies a fix there, and then watches production for recurrence.
Frequently asked questions
What is offline AI evaluation?
Offline evaluation runs a candidate system against curated or historical examples before deployment, often with expected outputs or human labels available.
What is online AI evaluation?
Online evaluation scores sampled production runs or conversations after deployment, usually without a single reference answer and with strict latency, privacy, and cost limits.
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